AI0-001 AI Security, Ethics and Governance Practice Question
During a penetration test, a security engineer discovers that an AI-powered chatbot can be tricked into revealing sensitive customer data by using specially crafted prompts. What type of attack is this, and what is the best mitigation?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between attacks that occur during training (e.g., data poisoning, model inversion) versus those that occur during inference (e.g., prompt injection), leading candidates to confuse the attack phase and choose a wrong mitigation.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Prompt injection attack; implement input validation and context sanitization
This is a prompt injection attack, where an attacker crafts inputs that cause the AI model to override its original instructions or constraints, leading to unintended behavior such as revealing sensitive data. The best mitigation is input validation and context sanitization, which filters or neutralizes malicious prompt content before it reaches the model, preventing the injection from succeeding.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Prompt injection attack; implement input validation and context sanitization
Why this is correct
Crafted prompts that override instructions and leak sensitive data constitute prompt injection. Because the model cannot distinguish trusted instructions from untrusted user content, mitigation requires validating and sanitising inputs, and isolating context, before the prompt reaches the model.
- ✗
Model inversion attack; apply differential privacy during training
Why it's wrong here
Model inversion reconstructs training inputs from model outputs, typically via repeated queries, not by tricking a chatbot with crafted prompts into disclosing stored customer data. Differential privacy during training would be apt if the concern were protecting training-set privacy, not blocking prompt injection at inference.
- ✗
Data poisoning attack; implement strict access controls
Why it's wrong here
Crafted prompts manipulating a live chatbot's responses are prompt injection, not data poisoning, which corrupts training data before deployment. Access controls do not stop injection through legitimate input channels. Data poisoning would be the answer if attackers were altering the model's training corpus rather than its runtime prompts.
- ✗
Membership inference attack; add noise to model outputs
Why it's wrong here
Membership inference determines whether a specific record was in the training set; it does not extract customer data through crafted prompts. Output noise addresses inference leakage, not prompt-driven disclosure. Membership inference would be correct if the goal were confirming an individual's presence in training data.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.